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Shape Measure for Identifying Perceptually Informative Parts of 3D Objects
 

Summary: Shape Measure for Identifying Perceptually
Informative Parts of 3D Objects
Sreenivas Sukumar, David Page, Andrei Gribok, Andreas Koschan, and Mongi Abidi
Imaging, Robotics, and Intelligent Systems Laboratory, The University of Tennessee
{ssrangan, dpage, agribok, akoschan, abidi} @utk.edu
Abstract
We propose a mathematical approach for
quantifying shape complexity of 3D surfaces based on
perceptual principles of visual saliency. Our curvature
variation measure (CVM), as a 3D feature, combines
surface curvature and information theory by
leveraging bandwidth-optimized kernel density
estimators. Using a part decomposition algorithm for
digitized 3D objects, represented as triangle meshes,
we apply our shape measure to transform the low level
mesh representation into a perceptually informative
form. Further, we analyze the effects of noise,
sensitivity to digitization, occlusions, and
descriptiveness to demonstrate our shape measure on
laser-scanned real world 3D objects.

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee

 

Collections: Computer Technologies and Information Sciences